CrossTransformer (CTX) [1]}: CTX and related approaches [2]} are more similar to FRN, in that they explicitly produce class-wise linear reconstructions \(\bar{Q}_c = \bar{W}S_c\) .
However, rather than solving for \(\bar{W}\) , these methods approximate it using attention and extra learned projection layers.
CTX reprojects the feature pools \(S_c\)  and \(Q\)  into two different “key" and “value" subspaces, yielding \(S_1,Q_1\)  and \(S_2,Q_2\) . The reconstruction of \(Q_2\)  is given by:
\(\bar{Q}_2 &= \sigma (\frac{1}{\sqrt{d}}Q_1S_1^T)S_2\) 
